Fast and Efficient Compression of Floating-Point Data
Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly growing in size, this increasingly leads to I/O bottlenecks. When the rate at which data is produced exceeds the available I/O bandwidth, th...
Uložené v:
| Vydané v: | IEEE transactions on visualization and computer graphics Ročník 12; číslo 5; s. 1245 - 1250 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
IEEE
01.09.2006
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 1077-2626, 1941-0506 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly growing in size, this increasingly leads to I/O bottlenecks. When the rate at which data is produced exceeds the available I/O bandwidth, the simulation stalls and the CPUs are idle. Data compression can alleviate this problem by using some CPU cycles to reduce the amount of data needed to be transfered. Most compression schemes, however, are designed to operate offline and seek to maximize compression, not throughput. Furthermore, they often require quantizing floating-point values onto a uniform integer grid, which disqualifies their use in applications where exact values must be retained. We propose a simple scheme for lossless, online compression of floating-point data that transparently integrates into the I/O of many applications. A plug-in scheme for data-dependent prediction makes our scheme applicable to a wide variety of data used in visualization, such as unstructured meshes, point sets, images, and voxel grids. We achieve state-of-the-art compression rates and speeds, the latter in part due to an improved entropy coder. We demonstrate that this significantly accelerates I/O throughput in real simulation runs. Unlike previous schemes, our method also adapts well to variable-precision floating-point and integer data |
|---|---|
| AbstractList | Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly growing in size, this increasingly leads to I/O bottlenecks. When the rate at which data is produced exceeds the available I/O bandwidth, the simulation stalls and the CPUs are idle. Data compression can alleviate this problem by using some CPU cycles to reduce the amount of data needed to be transfered. Most compression schemes, however, are designed to operate offline and seek to maximize compression, not throughput. Furthermore, they often require quantizing floating-point values onto a uniform integer grid, which disqualifies their use in applications where exact values must be retained. We propose a simple scheme for lossless, online compression of floating-point data that transparently integrates into the I/O of many applications. A plug-in scheme for data-dependent prediction makes our scheme applicable to a wide variety of data used in visualization, such as unstructured meshes, point sets, images, and voxel grids. We achieve state-of-the-art compression rates and speeds, the latter in part due to an improved entropy coder. We demonstrate that this significantly accelerates I/O throughput in real simulation runs. Unlike previous schemes, our method also adapts well to variable-precision floating-point and integer data.Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly growing in size, this increasingly leads to I/O bottlenecks. When the rate at which data is produced exceeds the available I/O bandwidth, the simulation stalls and the CPUs are idle. Data compression can alleviate this problem by using some CPU cycles to reduce the amount of data needed to be transfered. Most compression schemes, however, are designed to operate offline and seek to maximize compression, not throughput. Furthermore, they often require quantizing floating-point values onto a uniform integer grid, which disqualifies their use in applications where exact values must be retained. We propose a simple scheme for lossless, online compression of floating-point data that transparently integrates into the I/O of many applications. A plug-in scheme for data-dependent prediction makes our scheme applicable to a wide variety of data used in visualization, such as unstructured meshes, point sets, images, and voxel grids. We achieve state-of-the-art compression rates and speeds, the latter in part due to an improved entropy coder. We demonstrate that this significantly accelerates I/O throughput in real simulation runs. Unlike previous schemes, our method also adapts well to variable-precision floating-point and integer data. Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly growing in size, this increasingly leads to I/O bottlenecks. When the rate at which data is produced exceeds the available I/O bandwidth, the simulation stalls and the CPUs are idle. Data compression can alleviate this problem by using some CPU cycles to reduce the amount of data needed to be transfered. Most compression schemes, however, are designed to operate offline and seek to maximize compression, not throughput. Furthermore, they often require quantizing floating-point values onto a uniform integer grid, which disqualifies their use in applications where exact values must be retained. We propose a simple scheme for lossless, online compression of floating-point data that transparently integrates into the I/O of many applications. A plug-in scheme for data-dependent prediction makes our scheme applicable to a wide variety of data used in visualization, such as unstructured meshes, point sets, images, and voxel grids. We achieve state-of-the-art compression rates and speeds, the latter in part due to an improved entropy coder. We demonstrate that this significantly accelerates I/O throughput in real simulation runs. Unlike previous schemes, our method also adapts well to variable-precision floating-point and integer data A plug-in scheme for data-dependent prediction makes our scheme applicable to a wide variety of data used in visualization, such as unstructured meshes, point sets, images, and voxel grids. Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly growing in size, this increasingly leads to I/O bottlenecks. When the rate at which data is produced exceeds the available I/O bandwidth, the simulation stalls and the CPUs are idle. Data compression can alleviate this problem by using some CPU cycles to reduce the amount of data needed to be transfered. Most compression schemes, however, are designed to operate offline and seek to maximize compression, not throughput. Furthermore, they often require quantizing floating-point values onto a uniform integer grid, which disqualifies their use in applications where exact values must be retained. We propose a simple scheme for lossless, online compression of floating-point data that transparently integrates into the I/O of many applications. A plug-in scheme for data-dependent prediction makes our scheme applicable to a wide variety of data used in visualization, such as unstructured meshes, point sets, images, and voxel grids. We achieve state-of-the-art compression rates and speeds, the latter in part due to an improved entropy coder. We demonstrate that this significantly accelerates I/O throughput in real simulation runs. Unlike previous schemes, our method also adapts well to variable-precision floating-point and integer data. |
| Author | Lindstrom, P. Isenburg, M. |
| Author_xml | – sequence: 1 givenname: P. surname: Lindstrom fullname: Lindstrom, P. organization: Lawrence Livermore Nat. Lab., Berkeley, CA – sequence: 2 givenname: M. surname: Isenburg fullname: Isenburg, M. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/17080858$$D View this record in MEDLINE/PubMed |
| BookMark | eNp90b9rGzEUB3BRUuok7dgpEI4O6XTue_p10hjcOCkY2sHJKuQ7KSicJed0HvrfV8aOB0MzSaDPe6Dv94KcxRQdIV8RpoigfyyfZvdTCiCnyNkHco6aYw0C5Fm5Q9PUVFI5IRc5vwAg50p_IhNsQIES6pyIuc1jZWNX3Xkf2uDiWM3SejO4nEOKVfLVvE92DPG5_pNCef1pR_uZfPS2z-7L4bwkj_O75eyhXvy-_zW7XdQtZ3KsW60bL3inGaAA3nCBVLe4clZ5DZ1nRVEJViF3VmPXNJ5y4WSrV5QiCHZJvu_3bob0unV5NOuQW9f3Nrq0zUZpiUoLhCJv3pVSlbSkbgr8dgJf0naI5RdGSUGZEEwVdH1A29XadWYzhLUd_pq33Apge9AOKefBedOGsaSU4jjY0BsEs2vH7Noxu3ZMaadM1SdTx8X_8Vd7H5xzR8tLmFwp9g-bFpTl |
| CODEN | ITVGEA |
| CitedBy_id | crossref_primary_10_3390_a12090197 crossref_primary_10_1109_TC_2008_131 crossref_primary_10_1049_iet_ipr_2018_6602 crossref_primary_10_1016_j_jcp_2020_109704 crossref_primary_10_1109_MCG_2018_011461533 crossref_primary_10_1016_j_future_2024_05_022 crossref_primary_10_1177_1094342019853336 crossref_primary_10_1145_3605359 crossref_primary_10_1109_ACCESS_2020_2980006 crossref_primary_10_1109_TC_2023_3297442 crossref_primary_10_1007_s11042_024_18765_0 crossref_primary_10_1007_s41060_019_00180_6 crossref_primary_10_1016_j_cageo_2016_04_009 crossref_primary_10_1109_TCBB_2024_3366240 crossref_primary_10_1109_TVCG_2018_2864853 crossref_primary_10_3390_app12136718 crossref_primary_10_1111_j_1467_8659_2009_01378_x crossref_primary_10_1109_TPDS_2019_2894404 crossref_primary_10_1109_TPDS_2022_3168386 crossref_primary_10_1109_MCG_2021_3089627 crossref_primary_10_1016_j_jocs_2022_101615 crossref_primary_10_1177_10943420221085000 crossref_primary_10_1145_3762672 crossref_primary_10_1109_TVCG_2011_268 crossref_primary_10_1109_JSTARS_2024_3476990 crossref_primary_10_1080_00401706_2015_1027068 crossref_primary_10_1109_JIOT_2024_3365306 crossref_primary_10_1109_LGRS_2025_3562933 crossref_primary_10_1016_j_engappai_2024_108267 crossref_primary_10_1109_TVCG_2021_3114815 crossref_primary_10_1109_ACCESS_2023_3281834 crossref_primary_10_1145_3476831 crossref_primary_10_1109_ACCESS_2020_3014979 crossref_primary_10_1109_TBDATA_2022_3201176 crossref_primary_10_1016_j_ins_2023_119490 crossref_primary_10_1016_j_jcp_2022_111457 crossref_primary_10_1080_01621459_2017_1395339 crossref_primary_10_1016_j_jcp_2022_111577 crossref_primary_10_1109_TC_2021_3092201 crossref_primary_10_1016_j_is_2017_10_007 crossref_primary_10_1016_j_cose_2022_102910 crossref_primary_10_1109_ACCESS_2020_2989430 crossref_primary_10_1109_TVCG_2022_3214821 crossref_primary_10_1016_j_jer_2024_02_018 crossref_primary_10_1109_JIOT_2025_3554999 crossref_primary_10_1109_JSTSP_2013_2269272 crossref_primary_10_1145_3626717 crossref_primary_10_1109_TVCG_2012_274 crossref_primary_10_1109_TVCG_2014_2346324 crossref_primary_10_1007_s12650_018_0519_x crossref_primary_10_1016_j_micpro_2022_104453 crossref_primary_10_3390_s23073545 crossref_primary_10_1038_s43588_021_00156_2 crossref_primary_10_1111_j_1467_8659_2011_01964_x crossref_primary_10_3390_iot5040037 crossref_primary_10_1002_spe_3041 crossref_primary_10_1007_s00791_018_00303_9 crossref_primary_10_1016_j_jterra_2024_100967 crossref_primary_10_1109_TCSVT_2014_2372291 crossref_primary_10_1111_cgf_15097 crossref_primary_10_1109_TVCG_2014_2346458 crossref_primary_10_1109_ACCESS_2020_3000767 crossref_primary_10_1145_3478513_3480492 crossref_primary_10_1109_TVCG_2013_126 crossref_primary_10_1007_s42514_025_00229_y crossref_primary_10_1016_j_cageo_2020_104599 crossref_primary_10_3390_s21217190 crossref_primary_10_1007_s00366_023_01805_y crossref_primary_10_1137_19M126904X crossref_primary_10_1137_16M1086248 crossref_primary_10_1186_s12859_024_05907_2 crossref_primary_10_4018_ijcini_2013040104 crossref_primary_10_1109_TPDS_2019_2938503 crossref_primary_10_1007_s10915_024_02466_9 crossref_primary_10_1016_j_procs_2014_08_228 crossref_primary_10_1002_rob_22333 crossref_primary_10_1109_TMSCS_2018_2886851 crossref_primary_10_1177_1094342018762036 crossref_primary_10_69709_CAIC_2024_193132 crossref_primary_10_1007_s00778_025_00911_1 crossref_primary_10_1109_TC_2023_3257517 crossref_primary_10_1137_18M1168832 crossref_primary_10_1145_3053688 crossref_primary_10_1137_18M1166651 crossref_primary_10_1177_10943420241284023 crossref_primary_10_1016_j_jcp_2023_112636 crossref_primary_10_1016_j_cma_2024_117538 crossref_primary_10_1186_1687_5281_2007_085385 crossref_primary_10_1109_TPDS_2022_3194695 crossref_primary_10_1016_j_ins_2013_01_007 crossref_primary_10_1145_3585514 crossref_primary_10_1109_TPDS_2017_2749300 crossref_primary_10_1111_cgf_13336 crossref_primary_10_1587_transinf_E95_D_2778 crossref_primary_10_1016_j_neucom_2020_02_097 crossref_primary_10_1109_TUFFC_2022_3199173 crossref_primary_10_1111_cgf_13619 crossref_primary_10_1002_cpe_2887 crossref_primary_10_1109_TPDS_2018_2859932 crossref_primary_10_1002_spe_2524 crossref_primary_10_1109_LSP_2025_3576177 crossref_primary_10_1137_11082172X crossref_primary_10_3390_computation5020024 crossref_primary_10_1175_MWR_D_18_0170_1 crossref_primary_10_1109_TVCG_2020_3030381 crossref_primary_10_1007_s12650_014_0268_4 crossref_primary_10_1016_j_jpdc_2024_104955 crossref_primary_10_1145_3457207 crossref_primary_10_1109_TVCG_2012_194 crossref_primary_10_1111_cgf_13707 crossref_primary_10_3390_electronics11060858 crossref_primary_10_1109_TPS_2023_3268170 crossref_primary_10_1109_TVCG_2024_3432710 crossref_primary_10_1109_TNNLS_2019_2947380 crossref_primary_10_1109_TVCG_2019_2920130 crossref_primary_10_1007_s42044_025_00300_5 crossref_primary_10_1016_j_protcy_2012_10_063 crossref_primary_10_1051_matecconf_20167901076 crossref_primary_10_1002_fld_5344 crossref_primary_10_1186_s13640_017_0184_3 crossref_primary_10_1137_18M1208885 crossref_primary_10_1109_MCG_2011_102 crossref_primary_10_1007_s00371_009_0372_y crossref_primary_10_1016_j_asoc_2019_105741 crossref_primary_10_1109_JIOT_2020_3003468 crossref_primary_10_1109_TVCG_2007_70585 crossref_primary_10_1016_j_jksuci_2024_102246 crossref_primary_10_1109_TVCG_2024_3456337 crossref_primary_10_1038_s43588_021_00167_z crossref_primary_10_1109_TPDS_2022_3193867 crossref_primary_10_1016_j_jcp_2021_110686 crossref_primary_10_1111_j_1467_8659_2011_01989_x crossref_primary_10_1016_j_cad_2024_103732 |
| Cites_doi | 10.1109/DCC.2006.35 10.1006/gmod.2002.0575 10.1109/DCC.2004.1281484 10.1016/j.cad.2004.09.015 10.1109/DCC.2003.1194028 10.1145/1187112.1187276 10.1109/DCC.2000.838221 10.1109/VISUAL.2002.1183768 10.1109/SC.2005.70 10.1109/DCC.2004.1281488 10.1109/VISUAL.1999.809868 10.1016/S1524-0703(03)00044-4 10.1145/214762.214771 10.1109/DCC.2005.85 10.1117/12.564830 10.1111/1467-8659.00681 10.1111/j.1467-8659.2005.00872.x 10.1145/197938.197961 10.1109/VISUAL.2000.885711 10.1109/VISUAL.1996.568138 10.1109/DCC.2003.1194070 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006 |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 F28 FR3 |
| DOI | 10.1109/TVCG.2006.143 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic ANTE: Abstracts in New Technology & Engineering Engineering Research Database |
| DatabaseTitle | CrossRef PubMed Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional MEDLINE - Academic Engineering Research Database ANTE: Abstracts in New Technology & Engineering |
| DatabaseTitleList | MEDLINE - Academic Technology Research Database Technology Research Database PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1941-0506 |
| EndPage | 1250 |
| ExternalDocumentID | 2340924521 17080858 10_1109_TVCG_2006_143 4015488 |
| Genre | orig-research Journal Article |
| GroupedDBID | --- -~X .DC 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IEDLZ IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNI RNS RZB TN5 VH1 AAYXX CITATION AAYOK NPM RIG 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8 F28 FR3 |
| ID | FETCH-LOGICAL-c436t-c997f54d9301504745129c1bea8f90df3436260a814ea91d77f245e6c9b221053 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 294 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000241383300075&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1077-2626 |
| IngestDate | Sat Sep 27 19:10:10 EDT 2025 Thu Oct 02 17:16:29 EDT 2025 Sun Jun 29 16:31:25 EDT 2025 Fri Jun 06 19:40:10 EDT 2025 Tue Nov 18 22:34:46 EST 2025 Sat Nov 29 08:06:42 EST 2025 Wed Aug 27 02:47:56 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c436t-c997f54d9301504745129c1bea8f90df3436260a814ea91d77f245e6c9b221053 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 |
| PMID | 17080858 |
| PQID | 865235538 |
| PQPubID | 75741 |
| PageCount | 6 |
| ParticipantIDs | pubmed_primary_17080858 proquest_miscellaneous_896189510 proquest_miscellaneous_68110697 ieee_primary_4015488 proquest_journals_865235538 crossref_primary_10_1109_TVCG_2006_143 crossref_citationtrail_10_1109_TVCG_2006_143 |
| PublicationCentury | 2000 |
| PublicationDate | 2006-09-01 |
| PublicationDateYYYYMMDD | 2006-09-01 |
| PublicationDate_xml | – month: 09 year: 2006 text: 2006-09-01 day: 01 |
| PublicationDecade | 2000 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: New York |
| PublicationTitle | IEEE transactions on visualization and computer graphics |
| PublicationTitleAbbrev | TVCG |
| PublicationTitleAlternate | IEEE Trans Vis Comput Graph |
| PublicationYear | 2006 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 Liebchen (ref20) 2005 ref14 ref31 ref11 ref10 Isenburg (ref15) 2006 Schwan (ref24) ref2 ref17 ref16 ref19 (ref30) 2004 ref18 Subbotin (ref26) 1999 Schindler (ref23) 2000 ref25 ref22 ref28 ref29 ref8 ref7 ref9 ref4 ref6 ref5 Chen (ref3) 2005 Martin (ref21) Touma (ref27) 1998 |
| References_xml | – ident: ref22 doi: 10.1109/DCC.2006.35 – ident: ref19 doi: 10.1006/gmod.2002.0575 – volume-title: Video and Data Recording Conference ident: ref21 article-title: Range encoding: an algorithm for removing redundancy from a digitized message – start-page: 124 year: 2005 ident: ref3 article-title: Lossless compression of point-based 3D models publication-title: Pacific Graphics – ident: ref25 doi: 10.1109/DCC.2004.1281484 – ident: ref16 doi: 10.1016/j.cad.2004.09.015 – volume-title: Carryless Rangecoder year: 1999 ident: ref26 – ident: ref4 doi: 10.1109/DCC.2003.1194028 – ident: ref17 doi: 10.1145/1187112.1187276 – ident: ref7 doi: 10.1109/DCC.2000.838221 – ident: ref13 doi: 10.1109/VISUAL.2002.1183768 – start-page: 401 volume-title: Linux Symposium ident: ref24 article-title: Lustre: Building a file system for 1, 000-node clusters – ident: ref5 doi: 10.1109/SC.2005.70 – ident: ref10 doi: 10.1109/DCC.2004.1281488 – start-page: 26 year: 1998 ident: ref27 article-title: Triangle mesh compression publication-title: Graphics Interface – ident: ref11 doi: 10.1109/VISUAL.1999.809868 – ident: ref14 doi: 10.1016/S1524-0703(03)00044-4 – ident: ref31 doi: 10.1145/214762.214771 – ident: ref2 doi: 10.1109/DCC.2005.85 – ident: ref9 doi: 10.1117/12.564830 – ident: ref12 doi: 10.1111/1467-8659.00681 – year: 2000 ident: ref23 article-title: Range Encoder version 1.3 – year: 2005 ident: ref20 article-title: The MPEG-4 audio lossless coding (ALS) standard — Technology and applications publication-title: 119th Audio Engineering Society Convention – start-page: 115 year: 2006 ident: ref15 article-title: Streaming compression of tetrahedral volume meshes publication-title: Graphics Interface – ident: ref18 doi: 10.1111/j.1467-8659.2005.00872.x – ident: ref8 doi: 10.1145/197938.197961 – ident: ref6 doi: 10.1109/VISUAL.2000.885711 – ident: ref28 doi: 10.1109/VISUAL.1996.568138 – volume-title: Visualization contest data set year: 2004 ident: ref30 – ident: ref29 doi: 10.1109/DCC.2003.1194070 |
| SSID | ssj0014489 |
| Score | 2.3375146 |
| Snippet | Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly... A plug-in scheme for data-dependent prediction makes our scheme applicable to a wide variety of data used in visualization, such as unstructured meshes, point... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1245 |
| SubjectTerms | Analytical models Bandwidth Central processing units Compressing Computer simulation Data compression Data visualization Entropy fast entropy coding file compaction for I/O efficiency File systems Floating point arithmetic High throughput Image coding Integers large scale simulation and visualization Large-scale systems lossless compression predictive coding Predictive models range coder Studies Throughput Visualization |
| Title | Fast and Efficient Compression of Floating-Point Data |
| URI | https://ieeexplore.ieee.org/document/4015488 https://www.ncbi.nlm.nih.gov/pubmed/17080858 https://www.proquest.com/docview/865235538 https://www.proquest.com/docview/68110697 https://www.proquest.com/docview/896189510 |
| Volume | 12 |
| WOSCitedRecordID | wos000241383300075&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0506 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014489 issn: 1077-2626 databaseCode: RIE dateStart: 19950101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwED4VxAAD70d4lAyICUPbOH6MqLQwIMQAqFvk2o6EhBJEU34_d04IDHRgi5RTYp3Pvu985_sAzgQ3UhknGHo_zrj2faa4laynPbpbI7kI3IAv9_LhQU0m-rEDF-1dGO99KD7zl_QYcvmutHM6KrviAWCrJViSUtR3tdqMAYYZuq4vlGyAKP2nn-bV08vwtk47IDigLqESYZIikvdfrihwqyyGmcHdjDf-N9BNWG9gZXxd28EWdHyxDWu_mg3uQDo2syo2hYtHoW0EfiGm3aAuhC3iMo_Hb6WhMmj2WL7i2xtTmV14Ho-ehnesIU1glieiYlZrmafc6YTOMrjk5NFtf-qNynXP5QmnBjQ9o_rcG913UuYDnnph9XSA4V-a7MFyURb-AGLlpJgiBLODxHGfGqWVVSqx3FmLqMJFcPGtv8w2HcWJ2OItC5FFT2ekeSK6FBhhJBGct-LvdSuNRYI7pNJWqNFmBEffk5M1C22WKYGRdIq7dgSn7VtcIZT2MIUv57NMKPyF0DKCeIGEItobgpoR7NeT_jPAxlYO_x7TEazWZzJUdHYMy9XH3J_Aiv2sXmcfXbTTieoGO_0CCqDfGw |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3JTsMwEB2VRQIO7EsoSw6IE6ZLHC9HBBQQpeJQELfItR2pUpUgmvL9eJwQONADt0geOdZ4mTee8TyAM0YVF8ow4qwfJVTaDhFUc9KW1plbxSnz3ICvfT4YiLc3-dyAi_otjLXWJ5_ZS_z0sXyT6xlelbWoB9hiAZaQOat6rVXHDJyjIcsMQ066Dqf_VNRsDV-v78rAg4MHWCeUO6AkkOb9lzHy7CrzgaY3OL2N_w11E9YrYBlelSthCxo224a1X-UGdyDuqWkRqsyEt75whOshxPOgTIXNwjwNe5NcYSI0ec7HrvVGFWoXXnq3w-t7UtEmEE0jVhAtJU9jamSEtxmUU7TpujOySqSybdKIYgmathIdapXsGM7TLo0t03LUdQ5gHO3BYpZn9gBCYTgbORCmu5GhNlZCCi1EpKnR2uEKE8DFt_4SXdUUR2qLSeJ9i7ZMUPNIdcmcjxEFcF6Lv5fFNOYJ7qBKa6FKmwE0vycnqbbaNBHM-dKxO7cDOK1b3R7BwIfKbD6bJky4XzDJAwjnSAgkvkGwGcB-Oek_A6zWyuHfYzqFlfvhUz_pPwwem7Ba3tBgCtoRLBYfM3sMy_qzGE8_Tvxq_QKQ_OF8 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Fast+and+efficient+compression+of+floating-point+data&rft.jtitle=IEEE+transactions+on+visualization+and+computer+graphics&rft.au=Lindstrom%2C+Peter&rft.au=Isenburg%2C+Martin&rft.date=2006-09-01&rft.issn=1077-2626&rft.volume=12&rft.issue=5&rft.spage=1245&rft_id=info:doi/10.1109%2FTVCG.2006.143&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1077-2626&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1077-2626&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1077-2626&client=summon |